The standard quality measure for online algorithms is the competitive ratio, which is, roughly speaking, the worst case ratio of the online performance to the optimal offline performance. However, for many online problems, the competitive ratio gives overly pessimistic results and/or fails to distinguish between algorithms that are known to perform very differently in practice. Many researchers have proposed variations on the competitive ratio to obtain more realistic results. These variations are often tailormade for specific online problems. The concept of the accommodating function applies to any online problem with some limited resource, such as bins, seats in a train, or pages in a cache. If a limited amount n of some resource is available, the accommodating function A(alpha) is the competitive ratio when input sequences are restricted to those for which the amount alpha n of resources suffices for an optimal offline algorithm. For all resource bounded problems, the standard competitive ratio is the limit of A(alpha) for alpha going towards infinity. The accommodating function was originally used only for alpha>=1. We focus on alpha<1, observe that the function now appears interesting for a greater variety of problems, and use it to make new distinctions between known algorithms and to find new ones.
